Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Image database retrieval using neural networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The broad objective of this work has been to achieve retrieval of images from large unconstrained databases using image content. The problem is typified by the need to locate a target image within a database where no numerical indexing terms exist. Here, retrieval is based on important features within in an image and uses sample images or user sketches to specify a query. A typical query might be framed as "Find all images similar to this one", for example. The aim of this work has been to show how neural networks can provide a practical, flexible and robust solution to this problem. A neural network is basically an adaptive information filter which can be used to extract the salient characteristics of a data set during a training phase. The transformation learnt by the network can map the images into compact indices which support very rapid fuzzy matching of images across the database. This learning process optimises the performance of the code with respect to the contents of the database. We assess the applicability of several neural network architectures and learning rules for a practical coding scheme and investigate how the system parameters affect the performance of the system. We introduce a novel learning law which has a number of advantages over existing paradigms. In-depth mathematical analysis and extensive empirical tests are used to corroborate the arguments presented throughout. This thesis aims to show the nature of the image retrieval problem, how current research trends attempt to tackle it and how neural networks can offer us a real alternative to conventional approaches

GPT Models in Construction Industry: Opportunities, Limitations, and a Use Case Validation

Large Language Models(LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These models perform well on diverse tasks and have been gaining widespread applications in fields such as business and education. However, little is known about the opportunities and challenges of using LLMs in the construction industry. Thus, this study aims to assess GPT models in the construction industry. A critical review, expert discussion and case study validation are employed to achieve the study objectives. The findings revealed opportunities for GPT models throughout the project lifecycle. The challenges of leveraging GPT models are highlighted and a use case prototype is developed for materials selection and optimization. The findings of the study would be of benefit to researchers, practitioners and stakeholders, as it presents research vistas for LLMs in the construction industry.Comment: 58 pages, 20 figure

Information scraps: how and why information eludes our personal information management tools

In this paper we describe information scraps -- a class of personal information whose content is scribbled on Post-it notes, scrawled on corners of random sheets of paper, buried inside the bodies of e-mail messages sent to ourselves, or typed haphazardly into text files. Information scraps hold our great ideas, sketches, notes, reminders, driving directions, and even our poetry. We define information scraps to be the body of personal information that is held outside of its natural or We have much still to learn about these loose forms of information capture. Why are they so often held outside of our traditional PIM locations and instead on Post-its or in text files? Why must we sometimes go around our traditional PIM applications to hold on to our scraps, such as by e-mailing ourselves? What are information scraps' role in the larger space of personal information management, and what do they uniquely offer that we find so appealing? If these unorganized bits truly indicate the failure of our PIM tools, how might we begin to build better tools? We have pursued these questions by undertaking a study of 27 knowledge workers. In our findings we describe information scraps from several angles: their content, their location, and the factors that lead to their use, which we identify as ease of capture, flexibility of content and organization, and avilability at the time of need. We also consider the personal emotive responses around scrap management. We present a set of design considerations that we have derived from the analysis of our study results. We present our work on an application platform, jourknow, to test some of these design and usability findings

An Object-oriented expert system shell with image diagnosis.

by Chan Wai Kwong Samual.Thesis (M.Phil.)--Chinese University of Hong Kong.Bibliography: leaves R. 1-6.ACKNOWLEDGEMENTSABSTRACTTABLE OF CONTENTSChapter CHAPTER 1. --- OVERVIEWS --- p.1.1Chapter 1.1 --- Introduction --- p.1.1Chapter 1.2 --- Image Understanding and Artificial Intelligence --- p.1.3Chapter 1.3 --- Object-Oriented Programming and Artificial Intelligence --- p.1.6Chapter 1.4 --- Related Works --- p.1.8Chapter 1.5 --- Discussions and Outlines --- p.1.9Chapter CHAPTER 2. --- OBJECT-ORIENTED SOFTWARE SYSTEMS --- p.2.1Chapter 2.1 --- Introduction --- p.2.1Chapter 2.2 --- Traditional Software Systems --- p.2.1Chapter 2.3 --- Object-Oriented Software Systems --- p.2.2Chapter 2.4 --- Characteristics of an Object-Oriented Systems --- p.2.4Chapter 2.5 --- Knowledge Representation in Image Recognition --- p.2.9Chapter 2.5.1 --- Rule-Based System --- p.2.10Chapter 2.5.2 --- Structured Objects --- p.2.12Chapter 2.5.3 --- Object-Oriented Knowledge Management --- p.2.13Chapter 2.5.4 --- Object-Oriented Expert System Building Tools --- p.2.14Chapter 2.6 --- Concluding Remarks --- p.2.16Chapter CHAPTER 3. --- SYSTEM DESIGN AND ARCHITECTURE --- p.3.1Chapter 3.1 --- Introduction --- p.3.1Chapter 3.2 --- Inheritance and Recognition --- p.3.2Chapter 3.3 --- System Design --- p.3.9Chapter 3.4 --- System Architecture --- p.3.11Chapter 3.4.1 --- The Low Level Vision Kernel --- p.3.14Chapter 3.4.2 --- The High Level Vision Kernel --- p.3.15Chapter 3.4.3 --- User Consultation Kernel --- p.3.17Chapter 3.5 --- Structure of the Image Object Model --- p.3.17Chapter 3.5.1 --- Image Object Model in Object-Oriented Form --- p.3.19Chapter 3.5.2 --- Image Objects Hierarchy --- p.3.23Chapter 3.6 --- Reasoning in OOI --- p.3.26Chapter 3.7 --- Concluding Remarks --- p.3.27Chapter CHAPTER 4. --- CONTROL AND STRATEGIES --- p.4.1Chapter 4.1 --- Introduction --- p.4.1Chapter 4.2 --- Consultation Class Objects --- p.4.4Chapter 4.2.1 --- Audience --- p.4.5Chapter 4.2.2 --- Intrinsic Hypothesis (IH_object) --- p.4.5Chapter 4.2.3 --- Priority Table (PT_object) --- p.4.6Chapter 4.3 --- Operation Objects --- p.4.7Chapter 4.3.1 --- Scheme Scheduler (SS一object) --- p.4.7Chapter 4.3.2 --- Task Scheduler (TS_object) --- p.4.7Chapter 4.4 --- Taxonomy of Image Objects in OOI --- p.4.8Chapter 4.4.1 --- Object Template --- p.4.8Chapter 4.4.2 --- Attributes --- p.4.9Chapter 4.4.3 --- Tasks and Life Cycles --- p.4.9Chapter 4.4.4 --- Object Security --- p.4.10Chapter 4.5 --- Message Passing --- p.4.11Chapter 4.6 --- Strategies --- p.4.12Chapter 4.6.1 --- The Bottom-Up Approach --- p.4.15Chapter 4.6.2 --- The Top-Down Approach --- p.4.18Chapter 4.7 --- Concluding Remarks --- p.4.19Chapter CHAPTER 5. --- IMAGE PROCESSING ALGORITHMS --- p.5.1Chapter 5.1 --- Introduction --- p.5.1Chapter 5.2 --- Image Enhancement --- p.5.2Chapter 5.2.1 --- Spatial Filtering --- p.5.2Chapter 5.2.2 --- Feature Enhancement --- p.5.5Chapter 5.3 --- Pixel Classification --- p.5-7Chapter 5.4 --- Edge Detection Methods --- p.5.9Chapter 5.4.1 --- Local Gradient Operators --- p.5.9Chapter 5.4.2 --- Zero Crossing Method --- p.5.12Chapter 5.5 --- Regional Approaches in Segmentation --- p.5.13Chapter 5.5.1 --- Multi-level Threshold Method --- p.5.13Chapter 5.5.2 --- Region Growing --- p.5.15Chapter 5.6 --- Image Processing Techniques in Medical Domain --- p.5.17Chapter 5.7 --- Concluding Remarks --- p.5.18Chapter CHAPTER 6. --- PICTORIAL DATA MANAGEMENT IN OOI --- p.6.1Chapter 6.1 --- Introduction --- p.6.1Chapter 6.2 --- Description of Basic Properties --- p.6.1Chapter 6.3 --- Description of Relations --- p.6.7Chapter 6.3.1 --- Relational Database of Pictorial Data --- p.6.7Chapter 6.3.2 --- Relational Graphs and Relational Databases --- p.6.10Chapter 6.4 --- Access Functions in Image Objects --- p.6.14Chapter 6.4.1 --- Basic Access Functions --- p.6.14Chapter 6.4.2 --- User Accessible Functions in Objects --- p.6.15Chapter 6.5 --- Image Functions --- p.6.16Chapter 6.5.1 --- Unary Image operations --- p.6.16Chapter 6.5.2 --- Binary Relation Operations --- p.6.19Chapter 6.5.3 --- Update Operations --- p.6.20Chapter 6.6 --- Concluding Remarks --- p.6.21Chapter CHAPTER 7. --- KNOWLEDGE MANAGEMENT --- p.7.1Chapter 7.1 --- Introduction --- p.7.1Chapter 7.2 --- Knowledge in A Domain Knowledge Base --- p.7.1Chapter 7.2.1 --- Structure of Rules --- p.7.2Chapter 7.2.2 --- Hypothesis Generation --- p.7.6Chapter 7.2.3 --- Inference Engine --- p.7.8Chapter 7.3 --- Model Based Reasoning in OOI --- p.7.9Chapter 7.3.1 --- Merging and Labelling --- p.7.9Chapter 7.3.2 --- Vision Model --- p.7.11Chapter 7.4 --- Fuzzy Reasoning --- p.7.12Chapter 7.5 --- Concluding Remarks --- p.7.15Chapter CHAPTER 8. --- KNOWLEDGE ACQUISITION AND USER INTERFACES --- p.8.1Chapter 8.1 --- Introduction --- p.8.1Chapter 8.2 --- Knowledge Acquisition Subsystem --- p.8.3Chapter 8.2.1 --- Rule Management Module --- p.8.3Chapter 8.2.2 --- Attribute Management Module --- p.8.4Chapter 8.2.3 --- Model Management Module --- p.8.8Chapter 8.2.4 --- Methods of Knowledge Encoding and Acquisition --- p.8.9Chapter 8.3 --- User Interface in OOI --- p.8.11Chapter 8.3.1 --- Screen Layout --- p.8.13Chapter 8.3.2 --- Menus and Options --- p.8.15Chapter 8.4 --- Concluding Remarks --- p.8.20Chapter CHAPTER 9. --- IMPLEMENTATION AND RESULTS --- p.9.1Chapter 9.1 --- Introduction --- p.9.1Chapter 9.2 --- Using Expanded Memory --- p.9.2Chapter 9.3 --- ESCUM --- p.9.3Chapter 9.3.1 --- General Description --- p.9.3Chapter 9.3.2 --- Cervical Intraepithelial Neoplasia (CIN) --- p.9.4Chapter 9.3.3 --- Development of ESCUM --- p.9.5Chapter 9.4 --- Results --- p.9.12Chapter 9.5 --- Concluding Remarks --- p.9.13Chapter CHAPTER 10. --- CONCLUSION --- p.10.1Chapter 10.1 --- Summary --- p.10.1Chapter 10.2 --- Areas of Future Work --- p.10.5Chapter APPENDIX A. --- Rule Base of ESCUM --- p.A1Chapter APPENDIX B. --- Glossary for Objected-Oriented Programming --- p.B1REFERENCES --- p.R

Advances in Methodology and Applications of Decision Support Systems

These Proceedings are composed of a selection of papers of the Workshop on Advances in Methodology and Applications of Decision Support Systems, organized by the System and Decision Sciences (SDS) Program of IIASA and the Japan Institute of Systems Research (JISR). The workshop was held at IIASA on August 20-22, 1990. The Methodology of Decision Analysis (MDA) Project of the SDS Program focuses on a system-analytical approach to decision support and is devoted to developing methodology, software and applications of decision support systems concentrated primarily around interactive systems for data analysis, interpretation and multiobjective decisionmaking, including uncertainty analysis and group decision making situations in both their cooperative and noncooperative aspects. The objectives of the research on decision support systems (DSS) performed in cooperation with the MDA Project are to: compare various approaches to decision support systems; advance theory and methodology of decision support; convert existing theories and methodologies into usable (simple to use, user-friendly and robust) tools that could easily be used in solving real-life problems. A principal characteristic of decision support systems is that they must be tuned to specific decision situations, to complex real-life characteristics of every application. Even if the theory and methodology of decision support is quite advanced, every application might provide impulses for further theoretical and methodological advances. Therefore the principle underlying this project is that theoretical and methodological research should be strongly connected to the implementation and applications of its results to sufficiently complicated, real-life examples. This approach results in obtaining really applicable working tools for decision support. The papers for this Proceedings have been selected according to the above summarized framework of the research activities. Therefore, the papers deal both with theoretical and methodological problems and with real-life applications